Adaptive water resource planning using decision-rules

Dealing with uncertainty in water resource planning is problematic because insufficient or underused infrastructure can have social and environmental costs. Multistage stochastic optimisation provides a mechanism to deal with this challenge in water supply capacity expansion planning. However, for real systems it can be mathematically hard and computationally expensive. The ‘Decision-rule’ formulation represents an attempt to remedy this by approximating the multistage problem where decisions at each stage are a function of the uncertainty and the state of the system. We introduce a family of rules to show how they approximate the multistage problem and investigate the implications of the approximation for adaptive water resources planning

Screening reservoir systems by considering the efficient trade-offs-informing infrastructure investment decisions on the Blue Nile

Multi-reservoir system planners should consider how new dams impact downstream reservoirs and the potential contribution of each component to coordinated management. We propose an optimized multi-criteria screening approach to identify best performing designs, i.e., the selection, size and operating rules of new reservoirs within multi-reservoir systems. Reservoir release operating rules and storage sizes are optimized concurrently for each separate infrastructure design under consideration. Outputs reveal system trade-offs using multi-dimensional scatter plots where each point represents an approximately Pareto-optimal design. The method is applied to proposed Blue Nile River reservoirs in Ethiopia, where trade-offs between total and firm energy output, aggregate storage and downstream irrigation and energy provision for the best performing designs are evaluated. This proof-of concept study shows that recommended Blue Nile system designs would depend on whether monthly firm energy or annual energy is prioritized. 39 TWh/yr of energy potential is available from the proposed Blue Nile reservoirs. The results show that depending on the amount of energy deemed sufficient, the current maximum capacities of the planned reservoirs could be larger than they need to be. The method can also be used to inform which of the proposed reservoir type and their storage sizes would allow for the highest downstream benefits to Sudan in different objectives of upstream operating objectives (i.e., operated to maximize either average annual energy or firm energy). The proposed approach identifies the most promising system designs, reveals how they imply different trade-offs between metrics of system performance, and helps system planners asses the sensitivity of overall performance to the design parameters of component reservoirs

Balancing ecosystem services with energy and food security - assessing trade-offs for reservoir operation and irrigation investment in Kenya's Tana basin

Competition for water between key economic sectors and the environment means agreeing on allocation is challenging. Managing releases from the three major dams in Kenya’s Tana River basin with its 4.4 million inhabitants, 567MW of installed hydropower capacity, 33 000 ha of irrigation and ecologically important wetlands and forests is a pertinent example. This research seeks to identify and help decision-makers visualise reservoir management strategies which result in the best possible (Paretooptimal) allocation of benefits between sectors. Secondly we seek to show how tradeoffs between achievable benefits shift with the implementation of new proposed rice, cotton and biofuel irrigation projects. To identify the Pareto-optimal trade-offs we link a water resources management model to a multi-criteria search algorithm. The decisions or “levers” of the management problem are volume dependent release rules for the three major dams and extent of investment in new irrigation schemes. These decisions are optimised for objectives covering provision of water supply and irrigation, energy generation and maintenance of ecosystem services which underpin tourism and local livelihoods. Visual analytic plots allow decision makers to assess multi-reservoir rulesets by understanding their impacts on different beneficiaries. Results quantify how economic gains from proposed irrigation schemes trade-off against disturbance of the flow regime which supports ecosystem services. Full implementation of the proposed schemes is shown to be Pareto-optimal, but at high environmental and social cost. The clarity and comprehensiveness of “best-case” trade-off analysis is a useful vantage point from which to tackle the interdependence and complexity of water-energy-food “nexus” challenges

Screening robust water infrastructure investments and their trade-offs under global change: A London example

We propose an approach for screening future infrastructure and demand management investments for large water supply systems subject to uncertain future conditions. The approach is demonstrated using the London water supply system. Promising portfolios of interventions (e.g., new supplies, water conservation schemes, etc.) that meet London’s estimated water supply demands in 2035 are shown to face significant trade-offs between financial, engineering and environmental measures of performance. Robust portfolios are identified by contrasting the multi-objective results attained for (1) historically observed baseline conditions versus (2) future global change scenarios. An ensemble of global change scenarios is computed using climate change impacted hydrological flows, plausible water demands, environmentally motivated abstraction reductions, and future energy prices. The proposed multi-scenario trade-off analysis screens for robust investments that provide benefits over a wide range of futures, including those with little change. Our results suggest that 60 percent of intervention portfolios identified as Pareto optimal under historical conditions would fail under future scenarios considered relevant by stakeholders. Those that are able to maintain good performance under historical conditions can no longer be considered to perform optimally under future scenarios. The individual investment options differ significantly in their ability to cope with varying conditions. Visualizing the individual infrastructure and demand management interventions implemented in the Pareto optimal portfolios in multi-dimensional space aids the exploration of how the interventions affect the robustness and performance of the system

Trade-off informed adaptive and robust real options water resources planning

Planning water resource systems is challenged primarily by two realities. First, uncertainty is inherent in the predictions of future supplies and demands due for example to hydrological variability and climate change. To build societal resilience water planners should seek to enhance the adaptability and robustness of water resource system interventions. Second, water resource developments typically involve competing interests which implies considering the trade-offs and synergies implied by the highest performing combinations of development options is useful. This work describes a real options based planning framework that generates adaptive and robust water system design alternatives able to consider and trade-off different goals. The framework can address different types of uncertainties and suggests the highest performing designs across multiple evaluation criteria, such as financial costs and water supply service performance metrics. Using a global city's water resource and supply system as a demonstration of the approach, we explore the trade-offs between a long-term water management plan's infrastructure services (service resilience, reliability, vulnerability) and its financial costs under supply and demand uncertainty. The set of trade-off solutions consist of different investment plans which are adaptive and robust to future changing conditions. Results show that the highest performing plans lower net present value (NPV) of needed investments by up to 18%, while maintaining similar performance across the other objectives. The real option value of delaying investments as much as possible approaches up to 14% of total NPV

Using many-objective trade-off analysis to help dams promote economic development, protect the poor and enhance ecological health

Allocating water to different uses implies trading off the benefits perceived by different sectors. This paper demonstrates how visualising the trade-offs implied by the best performing water management options helps balance water use benefits and find sustainable solutions. The approach consists of linking a water resources model that can simulate many management policies and track diverse measures of system performance, to a many-objective evolutionary optimisation algorithm. This generates the set of Pareto-optimal management alternatives for several simultaneous objectives. The relative performance of these efficient management alternatives is then visualised as trade-off curves or surfaces using visual analytic plots. Visually assessing trade-offs between benefits helps select policies that achieve a decision-maker-selected balance between different metrics of system performance. We apply this approach to a multi-reservoir water resource system in Brazil's semi-arid Jaguaribe basin where current water allocation procedures favour sectors with greater political power and technical knowledge. The case study identifies promising reservoir operating policies by exploring trade-offs between economic, ecological and livelihood benefits as well as traditional hydropower generation, irrigation and water supply. Results show optimised policies can increase allocations to downstream uses while increasing median land availability for the poorest farmers by 25%

Real‐Options Water Supply Planning: Multistage Scenario Trees for Adaptive and Flexible Capacity Expansion Under Probabilistic Climate Change Uncertainty

Planning water supply infrastructure includes identifying interventions that cost‐effectively secure an acceptably reliable water supply. Climate change is a source of uncertainty for water supply developments as its impact on source yields is uncertain. Adaptability to changing future conditions is increasingly viewed as a valuable design principle of strategic water planning. Because present decisions impact a system's ability to adapt to future needs, flexibility in activating, delaying, and replacing engineering projects should be considered in least‐cost water supply intervention scheduling. This is a principle of Real Options Analysis, which this paper applies to least‐cost capacity expansion scheduling via multistage stochastic mathematical programming. We apply the proposed model to a real‐world utility with many investment decision stages using a generalized scenario tree construction algorithm to efficiently approximate the probabilistic uncertainty. To evaluate the implementation of Real Options Analysis, the use of two metrics is proposed: the value of the stochastic solution and the expected value of perfect information that quantify the value of adopting adaptive and flexible plans, respectively. An application to London's water system demonstrates the generalized approach. The investment decisions results are a mixture of long‐term and contingency schemes that are optimally chosen considering different futures. The value of the stochastic solution shows that by considering uncertainty, adaptive investment decisions avoid £100 million net present value (NPV) cost, 15% of the total NPV. The expected value of perfect information demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Sensitivity of results to the characteristics of the scenario tree and uncertainty set is assessed

Least Economic Cost Regional Water Supply Planning – Optimising Infrastructure Investments and Demand Management for South East England’s 17.6 Million People

This paper presents a deterministic capacity expansion optimisation model designed for large regional or national water supply systems. The annual model selects, sizes and schedules new options to meet predicted demands at minimum cost over a multi-year time horizon. Options include: supply-side schemes, demand management (water conservation) measures and bulk transfers. The problem is formulated as a mixed integer linear programming (MILP) optimisation model. Capital, operating, carbon, social and environmental costs of proposed discrete schemes are considered. User-defined annual water saving profiles for demand management schemes are allowed. Multiple water demand scenarios are considered simultaneously to ensure the supply–demand balance is preserved across high demand conditions and that variable costs are accurately assessed. A wide range of supplementary constraints are formulated to consider the interdependencies between schemes (pre-requisite, mutual exclusivity, etc.). A two-step optimisation scheme is introduced to prevent the infeasibilities that inevitably appear in real applications. The model was developed for and used by the ‘Water Resources in the South East’ stakeholder group to select which of the 316 available supply schemes (including imports) and 511 demand management options (considering 272 interdependencies) are to be activated to serve the inhabitants of South East of England. Selected schemes are scheduled and sized over a 25 year planning horizon. The model shows demand management options can play a significant role in the region’s water supply and should be considered alongside new supplies and regional transfers. Considering demand management schemes reduced overall total discounted economic costs by 10 % and removed two large reservoirs from the least-cost plan. This case-study optimisation model was built using a generalised data management software platform and solved using a mixed integer linear programme

A Model for Solving the Optimal Water Allocation Problem in River Basins with Network Flow Programming When Introducing Non-Linearities

[EN] The allocation of water resources between different users is a traditional problem in many river basins. The objective is to obtain the optimal resource distribution and the associated circulating flows through the system. Network flow programming is a common technique for solving this problem. This optimisation procedure has been used many times for developing applications for concrete water systems, as well as for developing complete decision support systems. As long as many aspects of a river basin are not purely linear, the study of non-linearities will also be of great importance in water resources systems optimisation. This paper presents a generalised model for solving the optimal allocation of water resources in schemes where the objectives are minimising the demand deficits, complying with the required flows in the river and storing water in reservoirs. Evaporation from reservoirs and returns from demands are considered, and an iterative methodology is followed to solve these two non-network constraints. The model was applied to the Duero River basin (Spain). Three different network flow algorithms (Out-of-Kilter, RELAX-IVand NETFLO) were used to solve the allocation problem. Certain convergence issues were detected during the iterative process. There is a need to relate the data from the studied systems with the convergence criterion to be able to find the convergence criterion which yields the best results possible without requiring a long calculation time.We thank the Spanish Ministry of Economy and Competitivity (Comision Interministerial de Ciencia y Tecnologia, CICYT) for funding the projects INTEGRAME (contract CGL2009-11798) and SCARCE (program Consolider-Ingenio 2010, project CSD2009-00065). 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